Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations690
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.4 KiB
Average record size in memory128.2 B

Variable types

Numeric9
Categorical7

Alerts

A8 is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with A8High correlation
A12 is highly imbalanced (68.4%) Imbalance
CustomerID has unique values Unique
A3 has 19 (2.8%) zeros Zeros
A7 has 70 (10.1%) zeros Zeros
A10 has 395 (57.2%) zeros Zeros
A13 has 132 (19.1%) zeros Zeros

Reproduction

Analysis started2025-03-11 00:05:58.358643
Analysis finished2025-03-11 00:06:05.595870
Duration7.24 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

Unique 

Distinct690
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15690471
Minimum15565714
Maximum15815443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:05.667441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15565714
5-th percentile15577630
Q115631687
median15690161
Q315751896
95-th percentile15801459
Maximum15815443
Range249729
Interquartile range (IQR)120209

Descriptive statistics

Standard deviation71506.474
Coefficient of variation (CV)0.0045573186
Kurtosis-1.161133
Mean15690471
Median Absolute Deviation (MAD)60397.5
Skewness-0.0029982826
Sum1.0826425 × 1010
Variance5.1131758 × 109
MonotonicityNot monotonic
2025-03-10T21:06:05.783092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15592412 1
 
0.1%
15776156 1
 
0.1%
15699294 1
 
0.1%
15615296 1
 
0.1%
15711759 1
 
0.1%
15672357 1
 
0.1%
15611794 1
 
0.1%
15606554 1
 
0.1%
15813192 1
 
0.1%
15730150 1
 
0.1%
Other values (680) 680
98.6%
ValueCountFrequency (%)
15565714 1
0.1%
15565996 1
0.1%
15566495 1
0.1%
15567834 1
0.1%
15567839 1
0.1%
15567860 1
0.1%
15567919 1
0.1%
15568162 1
0.1%
15568469 1
0.1%
15568819 1
0.1%
ValueCountFrequency (%)
15815443 1
0.1%
15815271 1
0.1%
15815095 1
0.1%
15815040 1
0.1%
15814116 1
0.1%
15813718 1
0.1%
15813363 1
0.1%
15813192 1
0.1%
15812918 1
0.1%
15812766 1
0.1%

A1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
1
468 
0
222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 468
67.8%
0 222
32.2%

Length

2025-03-10T21:06:05.872742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T21:06:05.933757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 468
67.8%
0 222
32.2%

Most occurring characters

ValueCountFrequency (%)
1 468
67.8%
0 222
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 468
67.8%
0 222
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 468
67.8%
0 222
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 468
67.8%
0 222
32.2%

A2
Real number (ℝ)

Distinct350
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.568203
Minimum13.75
Maximum80.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:06.006515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.75
5-th percentile17.956
Q122.67
median28.625
Q337.7075
95-th percentile56.231
Maximum80.25
Range66.5
Interquartile range (IQR)15.0375

Descriptive statistics

Standard deviation11.853273
Coefficient of variation (CV)0.37548139
Kurtosis1.1920586
Mean31.568203
Median Absolute Deviation (MAD)6.795
Skewness1.155935
Sum21782.06
Variance140.50008
MonotonicityNot monotonic
2025-03-10T21:06:06.108031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.57 12
 
1.7%
22.67 9
 
1.3%
20.42 7
 
1.0%
25 6
 
0.9%
19.17 6
 
0.9%
20.67 6
 
0.9%
23.58 6
 
0.9%
18.83 6
 
0.9%
24.5 6
 
0.9%
22.5 6
 
0.9%
Other values (340) 620
89.9%
ValueCountFrequency (%)
13.75 1
 
0.1%
15.17 1
 
0.1%
15.75 1
 
0.1%
15.83 2
0.3%
15.92 1
 
0.1%
16 2
0.3%
16.08 2
0.3%
16.17 1
 
0.1%
16.25 2
0.3%
16.33 3
0.4%
ValueCountFrequency (%)
80.25 1
0.1%
76.75 1
0.1%
74.83 1
0.1%
73.42 1
0.1%
71.58 1
0.1%
69.5 1
0.1%
69.17 1
0.1%
68.67 1
0.1%
67.75 1
0.1%
65.42 1
0.1%

A3
Real number (ℝ)

Zeros 

Distinct215
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7587246
Minimum0
Maximum28
Zeros19
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:06.207332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.165
Q11
median2.75
Q37.2075
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)6.2075

Descriptive statistics

Standard deviation4.9781632
Coefficient of variation (CV)1.0461129
Kurtosis2.2740219
Mean4.7587246
Median Absolute Deviation (MAD)2.21
Skewness1.4888131
Sum3283.52
Variance24.782109
MonotonicityNot monotonic
2025-03-10T21:06:06.303381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 21
 
3.0%
3 19
 
2.8%
2.5 19
 
2.8%
0 19
 
2.8%
0.75 16
 
2.3%
1.25 16
 
2.3%
0.5 15
 
2.2%
5 14
 
2.0%
6.5 12
 
1.7%
4 12
 
1.7%
Other values (205) 527
76.4%
ValueCountFrequency (%)
0 19
2.8%
0.04 5
 
0.7%
0.08 1
 
0.1%
0.085 1
 
0.1%
0.125 5
 
0.7%
0.165 8
1.2%
0.17 1
 
0.1%
0.205 3
 
0.4%
0.21 3
 
0.4%
0.25 6
 
0.9%
ValueCountFrequency (%)
28 1
0.1%
26.335 1
0.1%
25.21 1
0.1%
25.125 1
0.1%
25.085 1
0.1%
22.29 1
0.1%
22 1
0.1%
21.5 1
0.1%
21 1
0.1%
20 1
0.1%

A4
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
2
525 
1
163 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 525
76.1%
1 163
 
23.6%
3 2
 
0.3%

Length

2025-03-10T21:06:06.394625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T21:06:06.453171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 525
76.1%
1 163
 
23.6%
3 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 525
76.1%
1 163
 
23.6%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 525
76.1%
1 163
 
23.6%
3 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 525
76.1%
1 163
 
23.6%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 525
76.1%
1 163
 
23.6%
3 2
 
0.3%

A5
Real number (ℝ)

Distinct14
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3724638
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:06.508729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q310
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6832648
Coefficient of variation (CV)0.49959754
Kurtosis-0.84904258
Mean7.3724638
Median Absolute Deviation (MAD)3
Skewness-0.069190475
Sum5087
Variance13.566439
MonotonicityNot monotonic
2025-03-10T21:06:06.588716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8 146
21.2%
11 78
11.3%
9 64
9.3%
3 59
8.6%
6 54
 
7.8%
1 53
 
7.7%
4 51
 
7.4%
13 41
 
5.9%
7 38
 
5.5%
14 38
 
5.5%
Other values (4) 68
9.9%
ValueCountFrequency (%)
1 53
 
7.7%
2 30
 
4.3%
3 59
8.6%
4 51
 
7.4%
5 10
 
1.4%
6 54
 
7.8%
7 38
 
5.5%
8 146
21.2%
9 64
9.3%
10 25
 
3.6%
ValueCountFrequency (%)
14 38
 
5.5%
13 41
 
5.9%
12 3
 
0.4%
11 78
11.3%
10 25
 
3.6%
9 64
9.3%
8 146
21.2%
7 38
 
5.5%
6 54
 
7.8%
5 10
 
1.4%

A6
Real number (ℝ)

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6927536
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:06.655280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median4
Q35
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9923161
Coefficient of variation (CV)0.4245516
Kurtosis-0.17813233
Mean4.6927536
Median Absolute Deviation (MAD)0
Skewness0.46841183
Sum3238
Variance3.9693233
MonotonicityNot monotonic
2025-03-10T21:06:06.718931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 408
59.1%
8 138
 
20.0%
5 59
 
8.6%
1 57
 
8.3%
3 8
 
1.2%
9 8
 
1.2%
2 6
 
0.9%
7 6
 
0.9%
ValueCountFrequency (%)
1 57
 
8.3%
2 6
 
0.9%
3 8
 
1.2%
4 408
59.1%
5 59
 
8.6%
7 6
 
0.9%
8 138
 
20.0%
9 8
 
1.2%
ValueCountFrequency (%)
9 8
 
1.2%
8 138
 
20.0%
7 6
 
0.9%
5 59
 
8.6%
4 408
59.1%
3 8
 
1.2%
2 6
 
0.9%
1 57
 
8.3%

A7
Real number (ℝ)

Zeros 

Distinct132
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2234058
Minimum0
Maximum28.5
Zeros70
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:06.809589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.165
median1
Q32.625
95-th percentile8.56875
Maximum28.5
Range28.5
Interquartile range (IQR)2.46

Descriptive statistics

Standard deviation3.3465134
Coefficient of variation (CV)1.5051294
Kurtosis11.200192
Mean2.2234058
Median Absolute Deviation (MAD)0.915
Skewness2.8913304
Sum1534.15
Variance11.199152
MonotonicityNot monotonic
2025-03-10T21:06:06.918247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
10.1%
0.25 35
 
5.1%
0.04 33
 
4.8%
1 31
 
4.5%
0.125 30
 
4.3%
0.5 28
 
4.1%
0.085 26
 
3.8%
1.5 25
 
3.6%
0.165 22
 
3.2%
2.5 17
 
2.5%
Other values (122) 373
54.1%
ValueCountFrequency (%)
0 70
10.1%
0.04 33
4.8%
0.085 26
 
3.8%
0.125 30
4.3%
0.165 22
 
3.2%
0.21 6
 
0.9%
0.25 35
5.1%
0.29 12
 
1.7%
0.335 5
 
0.7%
0.375 7
 
1.0%
ValueCountFrequency (%)
28.5 1
 
0.1%
20 2
0.3%
18 1
 
0.1%
17.5 1
 
0.1%
16 1
 
0.1%
15.5 1
 
0.1%
15 3
0.4%
14.415 1
 
0.1%
14 3
0.4%
13.875 2
0.3%

A8
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
1
361 
0
329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 361
52.3%
0 329
47.7%

Length

2025-03-10T21:06:07.017913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T21:06:07.068867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 361
52.3%
0 329
47.7%

Most occurring characters

ValueCountFrequency (%)
1 361
52.3%
0 329
47.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 361
52.3%
0 329
47.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 361
52.3%
0 329
47.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 361
52.3%
0 329
47.7%

A9
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
0
395 
1
295 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 395
57.2%
1 295
42.8%

Length

2025-03-10T21:06:07.128386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T21:06:07.178430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 395
57.2%
1 295
42.8%

Most occurring characters

ValueCountFrequency (%)
0 395
57.2%
1 295
42.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 395
57.2%
1 295
42.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 395
57.2%
1 295
42.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 395
57.2%
1 295
42.8%

A10
Real number (ℝ)

Zeros 

Distinct23
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4
Minimum0
Maximum67
Zeros395
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:07.235044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile11
Maximum67
Range67
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.86294
Coefficient of variation (CV)2.026225
Kurtosis50.829431
Mean2.4
Median Absolute Deviation (MAD)0
Skewness5.1525199
Sum1656
Variance23.648186
MonotonicityNot monotonic
2025-03-10T21:06:07.311563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 395
57.2%
1 71
 
10.3%
2 45
 
6.5%
3 28
 
4.1%
6 23
 
3.3%
11 19
 
2.8%
5 18
 
2.6%
7 16
 
2.3%
4 15
 
2.2%
9 10
 
1.4%
Other values (13) 50
 
7.2%
ValueCountFrequency (%)
0 395
57.2%
1 71
 
10.3%
2 45
 
6.5%
3 28
 
4.1%
4 15
 
2.2%
5 18
 
2.6%
6 23
 
3.3%
7 16
 
2.3%
8 10
 
1.4%
9 10
 
1.4%
ValueCountFrequency (%)
67 1
 
0.1%
40 1
 
0.1%
23 1
 
0.1%
20 2
 
0.3%
19 1
 
0.1%
17 2
 
0.3%
16 3
 
0.4%
15 4
0.6%
14 8
1.2%
13 1
 
0.1%

A11
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
0
374 
1
316 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 374
54.2%
1 316
45.8%

Length

2025-03-10T21:06:07.396657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T21:06:07.446224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 374
54.2%
1 316
45.8%

Most occurring characters

ValueCountFrequency (%)
0 374
54.2%
1 316
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 374
54.2%
1 316
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 374
54.2%
1 316
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 374
54.2%
1 316
45.8%

A12
Categorical

Imbalance 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
2
625 
1
 
57
3
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 625
90.6%
1 57
 
8.3%
3 8
 
1.2%

Length

2025-03-10T21:06:07.513877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T21:06:07.569879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 625
90.6%
1 57
 
8.3%
3 8
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2 625
90.6%
1 57
 
8.3%
3 8
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 625
90.6%
1 57
 
8.3%
3 8
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 625
90.6%
1 57
 
8.3%
3 8
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 625
90.6%
1 57
 
8.3%
3 8
 
1.2%

A13
Real number (ℝ)

Zeros 

Distinct171
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.01449
Minimum0
Maximum2000
Zeros132
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:07.744117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180
median160
Q3272
95-th percentile460
Maximum2000
Range2000
Interquartile range (IQR)192

Descriptive statistics

Standard deviation172.15927
Coefficient of variation (CV)0.93557454
Kurtosis19.926698
Mean184.01449
Median Absolute Deviation (MAD)100
Skewness2.7499117
Sum126970
Variance29638.815
MonotonicityNot monotonic
2025-03-10T21:06:07.848761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
19.1%
120 35
 
5.1%
200 35
 
5.1%
160 34
 
4.9%
80 30
 
4.3%
100 30
 
4.3%
280 22
 
3.2%
180 18
 
2.6%
140 16
 
2.3%
320 14
 
2.0%
Other values (161) 324
47.0%
ValueCountFrequency (%)
0 132
19.1%
17 1
 
0.1%
20 2
 
0.3%
21 1
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
28 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
ValueCountFrequency (%)
2000 1
0.1%
1160 1
0.1%
980 1
0.1%
928 1
0.1%
840 1
0.1%
760 1
0.1%
720 2
0.3%
711 1
0.1%
680 1
0.1%
640 1
0.1%

A14
Real number (ℝ)

Distinct240
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1018.3855
Minimum1
Maximum100001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-03-10T21:06:07.955415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q3396.5
95-th percentile4120.4
Maximum100001
Range100000
Interquartile range (IQR)395.5

Descriptive statistics

Standard deviation5210.1026
Coefficient of variation (CV)5.1160416
Kurtosis214.66997
Mean1018.3855
Median Absolute Deviation (MAD)5
Skewness13.140655
Sum702686
Variance27145169
MonotonicityNot monotonic
2025-03-10T21:06:08.061066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 295
42.8%
2 29
 
4.2%
501 10
 
1.4%
1001 10
 
1.4%
3 9
 
1.3%
301 8
 
1.2%
6 8
 
1.2%
7 8
 
1.2%
201 6
 
0.9%
4 6
 
0.9%
Other values (230) 301
43.6%
ValueCountFrequency (%)
1 295
42.8%
2 29
 
4.2%
3 9
 
1.3%
4 6
 
0.9%
5 5
 
0.7%
6 8
 
1.2%
7 8
 
1.2%
8 4
 
0.6%
9 2
 
0.3%
10 1
 
0.1%
ValueCountFrequency (%)
100001 1
0.1%
51101 1
0.1%
50001 1
0.1%
31286 1
0.1%
26727 1
0.1%
18028 1
0.1%
15109 1
0.1%
15001 1
0.1%
13213 1
0.1%
11203 1
0.1%

Class
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
0
383 
1
307 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters690
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 383
55.5%
1 307
44.5%

Length

2025-03-10T21:06:08.152714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T21:06:08.201766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 383
55.5%
1 307
44.5%

Most occurring characters

ValueCountFrequency (%)
0 383
55.5%
1 307
44.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 383
55.5%
1 307
44.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 383
55.5%
1 307
44.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 383
55.5%
1 307
44.5%

Interactions

2025-03-10T21:06:04.580684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:58.904189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.596621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.365162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.027334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.710945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.403999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.126375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.832670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.654343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:58.992182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.673575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.443046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.110462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.786616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.477955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.202128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.918433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.732191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.072998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.743181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.521110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.192765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.863170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.629287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.282134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.998352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.802238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.145025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.879869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.587373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.270215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.937788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.698277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.369503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.078447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.876752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.217707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.950478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.659939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.338879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.013275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.767875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.447604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.178026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.956271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.291757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.035240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.736592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.417513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.089882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.838419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.528047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.264596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:05.030795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.363367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.109362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.806170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.484561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.167439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.905002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.601248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.341255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:05.109315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.440024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.199556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.879170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.560162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.246015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.976896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.677303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.423772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:05.192727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:05:59.520957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.286560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:00.957822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:01.639816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:02.328339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.055177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:03.759936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T21:06:04.501667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-10T21:06:08.258363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A1A10A11A12A13A14A2A3A4A5A6A7A8A9ClassCustomerID
A11.0000.0000.0340.0510.1070.0030.0730.0570.0350.2870.1650.0140.0000.0470.0000.000
A100.0001.0000.0850.000-0.1340.4270.1180.2040.0710.2230.1000.3150.3380.4550.3700.014
A110.0340.0851.0000.0520.1760.0100.0950.1000.0240.0670.1150.1190.0800.0000.0000.129
A120.0510.0000.0521.0000.0840.2330.0600.1000.1820.1860.0910.0000.1390.2430.1020.031
A130.107-0.1340.1760.0841.000-0.065-0.009-0.2940.0560.0580.081-0.0370.0470.0000.030-0.029
A140.0030.4270.0100.233-0.0651.0000.0400.1060.4930.1200.0670.0870.0870.0130.1160.023
A20.0730.1180.0950.060-0.0090.0401.0000.1150.064-0.0580.0930.2560.2170.1060.1590.013
A30.0570.2040.1000.100-0.2940.1060.1151.0000.2200.0500.1050.2660.2370.1680.2220.054
A40.0350.0710.0240.1820.0560.4930.0640.2201.0000.0690.2000.1300.1410.1700.1890.000
A50.2870.2230.0670.1860.0580.120-0.0580.0500.0691.0000.3280.2370.2970.2330.355-0.056
A60.1650.1000.1150.0910.0810.0670.0930.1050.2000.3281.0000.3290.2510.0670.235-0.044
A70.0140.3150.1190.000-0.0370.0870.2560.2660.1300.2370.3291.0000.2940.1710.2790.052
A80.0000.3380.0800.1390.0470.0870.2170.2370.1410.2970.2510.2941.0000.4280.7170.106
A90.0470.4550.0000.2430.0000.0130.1060.1680.1700.2330.0670.1710.4281.0000.4540.000
Class0.0000.3700.0000.1020.0300.1160.1590.2220.1890.3550.2350.2790.7170.4541.0000.031
CustomerID0.0000.0140.1290.031-0.0290.0230.0130.0540.000-0.056-0.0440.0520.1060.0000.0311.000

Missing values

2025-03-10T21:06:05.407411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-10T21:06:05.527143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustomerIDA1A2A3A4A5A6A7A8A9A10A11A12A13A14Class
015776156122.0811.4602441.5850001210012130
115739548022.677.0002840.1650000216010
215662854029.581.7501441.2500001228010
315687688021.6711.5001530.000111112011
415715750120.178.1702641.960111402601591
515571121015.830.5852881.5001120210011
615726466117.426.5002340.12500002601010
715660390058.674.46021183.04011602435611
815663942127.831.0001283.000000021765380
915638610055.757.0802486.75011312100510
CustomerIDA1A2A3A4A5A6A7A8A9A10A11A12A13A14Class
68015790689121.170.0002840.50000011010
68115665181135.2516.5001844.000100028010
68215633608022.9211.58521340.040100028013501
68315805261048.171.3352370.3350000201210
68415740356143.000.29011381.750118021003761
68515808223131.5710.50021446.50010002011
68615769980120.670.4152840.125000020450
68715675450018.839.5402640.0851000210011
68815776494027.4214.50021483.08511102120121
68915592412141.000.04021040.0400110156011